Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction.

Clinical outcome prediction based on Electronic Health Record (EHR) helps enable early interventions for high-risk patients, and is thus a central task for smart healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the long and irregular clinical event sequences in EHR. We make the observation that clinical events at a long time scale exhibit strong temporal patterns, while events within a short time period tend to be disordered co-occurrence.

Thickness-controlled direct growth of nanographene and nanographite film on non-catalytic substrates.

Metal-catalyzed chemical vapor deposition (CVD) has been broadly employed for large-scale production of high-quality graphene. However, a following transfer process to targeted substrates is needed, which is incompatible with current silicon technology. We here report a new CVD approach to form nanographene and nanographite films with accurate thickness control directly on non-catalytic substrates such as silicon dioxide and quartz at 800 °C.

Learning Scalable Deep Kernels with Recurrent Structure.

Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes.